Molecular signaling in mechanobiology regulation by single-cell analyses using bioinformatics approach

Homeostasis in living animals and in plants is a self-regulating process by which an organism can maintain internal stability while adjusting to changing external conditions. Numerous molecular mechanisms are involved in these regulatory processes, including cell sensing and communication, intracellular and membrane excitation, and extracellular connections. Mechanobiology enhances cellular sensing genes and channels controlling cell-cell communication, intracellular and membrane excitation, and extracellular connections. The project will develop a single-cell multiplex in situ tagging (scMIST) system combined with advanced machine leaning algorithms through successive rounds of labeling and imaging to effectively achieve a multiplexity of thousands of data points using a common fluorescence microscope and a simple procedure in a typical biological laboratory setting, which has the potential to revolutionize the field of mechanical biology. The project will be committed to outreach and recruitment efforts targeting minority students and those interested in STEM fields. Enhancing cellular viability and motility induced by dynamic physical stimulation is one of the vital components and factors in the biological system’s response to mechano-transduction and regeneration. The project will focus on 1) generating an integrated database of cellular differentiation and adaptation through Ca^(2+) release, Wnt/beta-Catenin signaling, and T-cell immuno-pathway with and without Piezo1 mitigation, and inter- and intra-cellular communication, and cell differentiation; 2) evaluating dynamic loading on single-cell DNA-encoded sequencing by quantifying signaling proteins and surface markers from various cell and proteins; 3) developing machine learning and deep learning algorithms, using R-Studio and bioinformatics platforms; and 4) developing an AI-based framework for bioinformatics analysis to visualize high-dimensional data, classify cell subtypes by both functions and phenotypes, and determine the signaling networks of each subtypes. This study will further enhance our understanding of the impact of the external environment on the living system and its adaptation at the cellular, molecular, and protein levels. The results of the project will be made to public through the lab website, https://you.stonybrook.edu/qinlab/home/. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.